Mach. Learn. Knowl. Extr. 2018, 1(1), 121-137; https://doi.org/10.3390/make1010007 (registering DOI)
Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance
Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy
Department of Mathematics & Computer Science, University of Marburg, 35037 Marburg, Germany
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 15 May 2018 / Revised: 22 June 2018 / Accepted: 27 June 2018 / Published: 28 June 2018
In this article, a two-tiered 2D tool is described, called
diagrams, and this tool has been devised to support the assessment of classifiers in terms of accuracy and bias. In their standard versions, these diagrams provide information, as the underlying data were in fact balanced. Their generalization, i.e., ability to account for the imbalance, will be also briefly described. In either case, the isometrics of accuracy and bias are immediately evident therein, as—according to a specific design choice—they are in fact straight lines parallel to the x
-axis and y
diagrams can also be used to assess the importance of features, as highly discriminant ones are immediately evident therein. In this paper, a comprehensive introduction on how to adopt
diagrams as a standard tool for classifier and feature assessment is given. In particular, with the goal of illustrating all relevant details from a pragmatic perspective, their implementation and usage as Python and R packages will be described.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Armano, G.; Giuliani, A.; Neumann, U.; Rothe, N.; Heider, D. Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance. Mach. Learn. Knowl. Extr. 2018, 1, 121-137.
Armano G, Giuliani A, Neumann U, Rothe N, Heider D. Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance. Machine Learning and Knowledge Extraction. 2018; 1(1):121-137.
Armano, Giuliano; Giuliani, Alessandro; Neumann, Ursula; Rothe, Nikolas; Heider, Dominik. 2018. "Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance." Mach. Learn. Knowl. Extr. 1, no. 1: 121-137.
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